Data scientists

Resources for data scientists who want to boost their machine learning models with external data.

  • How to Improve Your Training Data for Vastly Better Machine Learning

    How to Improve Your Training Data for Vastly Better Machine Learning

    Making your training data better is much easier than you think, and you can use several easy strategies for quick wins.

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  • Why You Need Data Catalogs, Not Databases

    Why You Need Data Catalogs, Not Databases

    When it comes to external data for machine learning, data catalogs provide a handful of time-saving benefits over databases. Learn more.

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  • AI is Making BI Obsolete, and Machine Learning is Leading the Way

    AI is Making BI Obsolete, and Machine Learning is Leading the Way

    Why are we still hung up on BI? It’s time to embrace a paradigm that empowers us to make smarter, better predictions using real data with machine learning.

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  • Why Data Marketplaces Are the Future of the Data Economy

    Why Data Marketplaces Are the Future of the Data Economy

    Data marketplaces make the lives of data scientists looking for machine learning datasets much easier. Read how.

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  • How to Connect to the Data Ecosystem

    How to Connect to the Data Ecosystem

    In this article, we explain where external data comes from, the key challenges, and how to connect and utilize it with data science solutions.

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  • How Our COVID-19 Signals Give Businesses Better Decision-Making Capabilities

    How Our COVID-19 Signals Give Businesses Better Decision-Making Capabilities

    With ML models rendered useless, we built an entirely new set of COVID-19 signals in our platform that let organizations understand their risk derived from the current pandemic.

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  • Data Science Salon 2020 - The Next Frontier of Data Science: Automated Feature Generation19:15

    Data Science Salon 2020 - The Next Frontier of Data Science: Automated Feature Generation

    Dedy Kredo, Head of Customer Facing Data Science at Explorium, presents about augmented data and feature discovery at Data Science Salon Austin.

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  • Data Bias and What it Means for Your Machine Learning Models

    Data Bias and What it Means for Your Machine Learning Models

    Let’s take a look at some of the most prevalent types of bias, the data mistakes that cause them – and how to prevent this from happening in your own models.

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  • Want to Get People Excited About Your Machine Learning Project? Tell Them a Story

    Want to Get People Excited About Your Machine Learning Project? Tell Them a Story

    Top tips to engage stakeholders at every stage of the data science project life cycle.

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  • Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    Tomorrow Comes Today – Using External Data to Cut Through the Unpredictability of Crises

    It’s time to look outside of our own silos to external data and make sure that despite the crises we face, we can give our organizations the power to make it through. The post Tomorrow Comes Today –

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  • COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    COVID-19 Broke Your Risk Models. This is How External Data Can Fix Them.

    For lenders, pre-COVID-19 data is no longer useful in a post-COVID-19 world. Here's how we're giving our customers external data to ensure their risk models work in our new reality.

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  • Explorium: Product Overview1:03

    Explorium: Product Overview

    Explorium is the future of data science. We enable you to automatically connect to thousands of data sources, distill the best features, and deploy the best models.

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  • The Complete Guide For Data Acquisition

    The Complete Guide For Data Acquisition

    This complete guide breaks down data acquisition into six steps, including data provider due diligence and data provider tests to uplift your model's accuracy.

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  • 5 Steps to Mine Hidden Gold Out of An External Data Source

    5 Steps to Mine Hidden Gold Out of An External Data Source

    Extracting gold from an external data source is all about data prep. Follow these five steps and you’ll be well on your way to creating golden enrichments.

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  • How Data Scientists Can Get a Seat at the Strategy Table

    How Data Scientists Can Get a Seat at the Strategy Table

    How can you earn yourself a seat at the table when decisions are being made? It starts with making yourself accessible and invaluable. Read how you can get started now.

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  • Feature Generation: The Next Frontier of Data Science

    Feature Generation: The Next Frontier of Data Science

    It's time to take feature generation - a subset of feature engineering - from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.

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  • Understanding and Handling Data and Concept Drift

    Understanding and Handling Data and Concept Drift

    Over time, ML models start to lose predictive power due to a concept known as model drift. How can you spot data and concept drift and avoid it? Read more.

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  • The Essential Guide to Feature Selection

    The Essential Guide to Feature Selection

    Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong.

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  • Stay Inside The Lines: Coloring With Artificial Intelligence

    Stay Inside The Lines: Coloring With Artificial Intelligence

    Can you teach AI to color better than a human? Using a generative adversarial network we can certainly try.

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  • Support and Coverage – Data Integration Metrics You Should Know

    Support and Coverage – Data Integration Metrics You Should Know

    Data enrichment is a crucial step in the modeling process that data scientists tend to overlook due to the difficulty in finding and utilizing external sources.

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